192 citations · 423 across the 25 of their papers we have counts for
28 papers
A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference
Shuaicheng Niu, Guohao Chen, Yaofo Chen +14
The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution of a fixed trained model, a…
CATeye: Coupled Attribute-Topology Invariance Learning for Voucher Abuse Detection
Tian Tian, Shuaicheng Niu, Hao Kuang +3
Voucher abuse poses a major challenge in e-commerce, where malicious users exploit promotional vouchers for profit. Unfortunately, fraud patterns evolve rapidly over time and acros…
DriveFlow: Rectified Flow Adaptation for Robust 3D Object Detection in Autonomous Driving
Hongbin Lin, Yiming Yang, Chaoda Zheng +7
In autonomous driving, vision-centric 3D object detection recognizes and localizes 3D objects from RGB images. However, due to high annotation costs and diverse outdoor scenes, tra…
Adapt in the Wild: Test-Time Entropy Minimization with Sharpness and Feature Regularization
Shuaicheng Niu, Guohao Chen, Deyu Chen +7
Test-time adaptation (TTA) may fail to improve or even harm the model performance when test data have: 1) mixed distribution shifts, 2) small batch sizes, 3) online imbalanced labe…
ZeroSiam: An Efficient Asymmetry for Test-Time Entropy Optimization without Collapse
Guohao Chen, Shuaicheng Niu, Deyu Chen +5
Test-time entropy minimization helps adapt a model to novel environments and incentivize its reasoning capability, unleashing the model's potential during inference by allowing it…
Test-Time Model Adaptation for Quantized Neural Networks
Zeshuai Deng, Guohao Chen, Shuaicheng Niu +6
Quantizing deep models prior to deployment is a widely adopted technique to speed up inference for various real-time applications, such as autonomous driving. However, quantized mo…
When Small Guides Large: Cross-Model Co-Learning for Test-Time Adaptation
Chang'an Yi, Xiaohui Deng, Guohao Chen +3
Test-time Adaptation (TTA) adapts a given model to testing domain data with potential domain shifts through online unsupervised learning, yielding impressive performance. However,…
Self-Bootstrapping for Versatile Test-Time Adaptation
Shuaicheng Niu, Guohao Chen, Peilin Zhao +3
In this paper, we seek to develop a versatile test-time adaptation (TTA) objective for a variety of tasks - classification and regression across image-, object-, and pixel-level pr…
NullSwap: Proactive Identity Cloaking Against Deepfake Face Swapping
Tianyi Wang, Harry Cheng, Xiao Zhang +1
Suffering from performance bottlenecks in passively detecting high-quality Deepfake images due to the advancement of generative models, proactive perturbations offer a promising ap…
DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generation
Hongbin Lin, Zilu Guo, Yifan Zhang +5
In autonomous driving, vision-centric 3D detection aims to identify 3D objects from images. However, high data collection costs and diverse real-world scenarios limit the scale of…
Fully Test-Time Adaptation for Monocular 3D Object Detection
Hongbin Lin, Yifan Zhang, Shuaicheng Niu +2
Monocular 3D object detection (Mono 3Det) aims to identify 3D objects from a single RGB image. However, existing methods often assume training and test data follow the same distrib…
Test-Time Model Adaptation with Only Forward Passes
Shuaicheng Niu, Chunyan Miao, Guohao Chen +2
Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. However, in real-world scenarios, models are…
Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting
Mingkui Tan, Guohao Chen, Jiaxiang Wu +4
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and test data by adapting a given model w.r.t. any test sample. Although recent TTA has sh…
Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation
Yaofo Chen, Shuaicheng Niu, Yaowei Wang +3
The conventional deep learning paradigm often involves training a deep model on a server and then deploying the model or its distilled ones to resource-limited edge devices. Usuall…
Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction
Zeshuai Deng, Zhuokun Chen, Shuaicheng Niu +3
Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. Conventional SR methods typically gather th…
Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment
Hongbin Lin, Mingkui Tan, Yifan Zhang +5
Source-free Unsupervised Domain Adaptation (SF-UDA) aims to adapt a well-trained source model to an unlabeled target domain without access to the source data. One key challenge is…
Towards Stable Test-Time Adaptation in Dynamic Wild World
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang +4
Test-time adaptation (TTA) has shown to be effective at tackling distribution shifts between training and testing data by adapting a given model on test samples. However, the onlin…
Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation
Hongbin Lin, Yifan Zhang, Zhen Qiu +4
This paper studies a new, practical but challenging problem, called Class-Incremental Unsupervised Domain Adaptation (CI-UDA), where the labeled source domain contains all classes,…
Efficient Test-Time Model Adaptation without Forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang +4
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particu…
Boost Test-Time Performance with Closed-Loop Inference
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang +6
Conventional deep models predict a test sample with a single forward propagation, which, however, may not be sufficient for predicting hard-classified samples. On the contrary, we…
AdaXpert: Adapting Neural Architecture for Growing Data
Shuaicheng Niu, Jiaxiang Wu, Guanghui Xu +5
In real-world applications, data often come in a growing manner, where the data volume and the number of classes may increase dynamically. This will bring a critical challenge for…
Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation
Zhen Qiu, Yifan Zhang, Hongbin Lin +4
We study a practical domain adaptation task, called source-free unsupervised domain adaptation (UDA) problem, in which we cannot access source domain data due to data privacy issue…
Towards Accurate Text-based Image Captioning with Content Diversity Exploration
Guanghui Xu, Shuaicheng Niu, Mingkui Tan +3
Text-based image captioning (TextCap) which aims to read and reason images with texts is crucial for a machine to understand a detailed and complex scene environment, considering t…
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
Yifan Zhang, Ying Wei, Qingyao Wu +4
Deep learning based medical image diagnosis has shown great potential in clinical medicine. However, it often suffers two major difficulties in real-world applications: 1) only lim…